Fetal signatures in the 3D genome of iPSC-derived neurons and their implications for disease modeling.

Authors

Diana Zagirova, Anna Kononkova, Kirill Morozov, Maria Molodova, Nikita Vaulin, Anastasiia Dudkovskaia, Polina Dozorova, Olga Efimova, Anna Tvorogova, Kirill Ulianov, Philipp Khaitovich, Sergey Razin, Maria Lagarkova, Sergey Ulianov, Ekaterina Khrameeva

Published in

Genome research. Aug 03, 2026. Epub Aug 03, 2026.

Abstract

Induced pluripotent stem cells (iPSCs) have revolutionized neuroscience, providing an approach to generate patient-specific neurons for modeling of neurological diseases. However, it remains unclear how closely iPSC-derived neurons replicate the chromatin architecture of authentic brain neurons. Here, we uniformly process datasets for 228 human and 89 mouse Hi-C and Snm3C-seq samples of different cell subtypes merged into 96 high-coverage contact maps used to examine chromatin features ranging from chromatin compartments and topologically associating domains (TADs) to chromatin loops, Polycomb-mediated contacts, and frequently interacting regions (FIREs). We find that iPSC-derived neurons largely retain chromatin state of undifferentiated cells and resemble fetal rather than mature neurons. iPSC-derived neurons exhibit unusually strong compartmentalization, an enrichment of developmental genes at TAD borders, and a marked reduction of long-range repressive Polycomb-mediated contacts that typically silence early fetal programs. Although immature, iPSC-derived neurons offer advantages for modeling interactions between disease-associated SNPs and target genes, as many psychiatric disorders have neurodevelopmental origins. Integrating iPSC-derived and post-mortem neuronal datasets therefore provides complementary insights into the chromatin landscape underlying disease-associated interactions. Our study offers a valuable Hi-C resource for the community and provides a detailed comparison of chromatin architecture throughout neuronal maturation, underscoring its importance for validating neuronal models and providing a robust framework for future studies.

PMID:
42547299
Bibliographic data and abstract were imported from PubMed on 04 Aug 2026.

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